Non-asymptotic oracle inequalities for the high-dimensional cox regression via lasso

AbstractSummaryWe developed hdnom, an R package for survival modeling with high-dimensional data. The package is the first free and open-source software package that streamlines the workflow of penalized Cox model building, validation, calibration, comparison, and nomogram visualization, with nine types of penalized Cox regression methods fully supported. A web application and an online prediction tool maker are offered to enhance interac-tivity and flexibility in high-dimensional survival analysis.AvailabilityThe hdnom R package is available from CRAN:https://cran.r-project.org/package=hdnomunder GPL. The hdnom web application can be accessed athttp://hdnom.io. The web application maker is available fromhttp://hdnom.org/appmaker. The hdnom project website:http://[email protected]@duke.edu

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Prognostic scoring system for osteosarcoma using network‐regularized high‐dimensional Cox‐regression analysis and potential therapeutic targets

Journal of Cellular Physiology ◽

10.1002/jcp.28065 ◽

2019 ◽

Vol 234 (8) ◽

pp. 13851-13857 ◽

Cited By ~ 6

Author(s):

Tae Sik Goh ◽

Jung Sub Lee ◽

Jeung Il Kim ◽

Yong Geon Park ◽

Kyoungjune Pak ◽

...

Keyword(s):

Regression Analysis ◽

Scoring System ◽

Cox Regression ◽

Therapeutic Targets ◽

High Dimensional ◽

Cox Regression Analysis

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UNIFORM INFERENCE IN HIGH-DIMENSIONAL DYNAMIC PANEL DATA MODELS WITH APPROXIMATELY SPARSE FIXED EFFECTS

Econometric Theory ◽

10.1017/s0266466618000087 ◽

2018 ◽

Vol 35 (2) ◽

pp. 295-359 ◽

Cited By ~ 2

Author(s):

Anders Bredahl Kock ◽

Haihan Tang

Keyword(s):

Panel Data ◽

Fixed Effects ◽

Dynamic Panel Data ◽

High Dimensional ◽

Conditional Heteroskedasticity ◽

Panel Data Models ◽

Parameter Vector ◽

Dynamic Panel ◽

Oracle Inequalities ◽

Dynamic Panel Data Models

We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and exogenous regressors. Separate oracle inequalities are derived for the fixed effects. Next, we show how one can conduct uniformly valid inference on the parameters of the model and construct a uniformly valid estimator of the asymptotic covariance matrix which is robust to conditional heteroskedasticity in the error terms. Allowing for conditional heteroskedasticity is important in dynamic models as the conditional error variance may be nonconstant over time and depend on the covariates. Furthermore, our procedure allows for inference on high-dimensional subsets of the parameter vector of an increasing cardinality. We show that the confidence bands resulting from our procedure are asymptotically honest and contract at the optimal rate. This rate is different for the fixed effects than for the remaining parts of the parameter vector.

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